Pengelompokkan Kualitas Pemuda Menurut Provinsi di Indonesia Tahun 2023 Menggunakan Partition Clustering

The percentage of youth in Indonesia in the last decade has been in the range of 23-24 percent of the Indonesian population. Youth have an active role in national development. Therefore, youth development is the focus of the government because it is the key to accelerating development. This study aims to categorize provinces based on the condition of youth in health, education, and socio-economic aspects in 2023 so that it can assist the government in determining youth development policies effectively and inclusively. Based on the validation, k-medoids algorithm was chosen as the best clustering method with the number of clusters as many as 4. In general, the four clusters have quite different characteristics. Youth development in education, socio-economic, and health aspects by province still shows gaps. Therefore, equalization efforts tailored to the needs of provincial clusters are needed to achieve more equitable and inclusive progress.

Authors

Publication Details

Journal
Prosiding Seminar Nasional Sains Data.
Published
2024-09-26
DOI
https://doi.org/10.33005/senada.v4i1.192
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Pengelompokkan Kualitas Pemuda Menurut Provinsi di Indonesia Tahun 2023 Menggunakan Partition Clustering

Setia Pramana, Febi Susanto
Prosiding Seminar Nasional Sains Data.
Data Mining and Machine Learning Applications
article

Pengelompokkan Kualitas Pemuda Menurut Provinsi di Indonesia Tahun 2023 Menggunakan Partition Clustering

Setia Pramana, Febi Susanto
article en

Abstract

The percentage of youth in Indonesia in the last decade has been in the range of 23-24 percent of the Indonesian population. Youth have an active role in national development. Therefore, youth development is the focus of the government because it is the key to accelerating development. This study aims to categorize provinces based on the condition of youth in health, education, and socio-economic aspects in 2023 so that it can assist the government in determining youth development policies effectively and inclusively. Based on the validation, k-medoids algorithm was chosen as the best clustering method with the number of clusters as many as 4. In general, the four clusters have quite different characteristics. Youth development in education, socio-economic, and health aspects by province still shows gaps. Therefore, equalization efforts tailored to the needs of provincial clusters are needed to achieve more equitable and inclusive progress.

Prosiding Seminar Nasional Sains Data.Vol. 4(1)
Openalex Percentile: Top 66%
Data Mining and Machine Learning Applications
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